Overview

Dataset statistics

Number of variables10
Number of observations4238
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory314.6 KiB
Average record size in memory76.0 B

Variable types

NUM8
CAT1
DATE1

Reproduction

Analysis started2020-08-15 15:57:13.815784
Analysis finished2020-08-15 15:57:49.040155
Duration35.22 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Death is highly correlated with Total and 1 other fieldsHigh correlation
Total is highly correlated with Death and 1 other fieldsHigh correlation
Cured is highly correlated with Total and 1 other fieldsHigh correlation
New recovered is highly correlated with New casesHigh correlation
New cases is highly correlated with New recoveredHigh correlation
Death has 1435 (33.9%) zeros Zeros
Cured has 611 (14.4%) zeros Zeros
New cases has 1151 (27.2%) zeros Zeros
New deaths has 2703 (63.8%) zeros Zeros
New recovered has 1735 (40.9%) zeros Zeros

Variables

Date
Date

Distinct count173
Unique (%)4.1%
Missing0
Missing (%)0.0%
Memory size33.1 KiB
Minimum2020-01-30 00:00:00
Maximum2020-07-25 00:00:00
2020-08-15T21:27:49.592426image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:50.261667image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram

State
Categorical

Distinct count39
Unique (%)0.9%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Kerala
 
173
Delhi
 
141
Uttar Pradesh
 
139
Haryana
 
139
Rajasthan
 
139
Other values (34)
3507
ValueCountFrequency (%) 
Kerala1734.1%
 
Delhi1413.3%
 
Uttar Pradesh1393.3%
 
Haryana1393.3%
 
Rajasthan1393.3%
 
Tamil Nadu1363.2%
 
Maharashtra1343.2%
 
Punjab1343.2%
 
Karnataka1343.2%
 
Andhra Pradesh1313.1%
 
Other values (29)283867.0%
 
2020-08-15T21:27:50.878029image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length40
Median length9
Mean length10.36479471
Min length3

Latitude
Real number (ℝ≥0)

Distinct count35
Unique (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.171111986786226
Minimum10.8505
Maximum34.2996
Zeros0
Zeros (%)0.0%
Memory size33.1 KiB
2020-08-15T21:27:51.368732image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum10.8505
5-th percentile11.1271
Q118.1124
median23.6102
Q328.218
95-th percentile33.7782
Maximum34.2996
Range23.4491
Interquartile range (IQR)10.1056

Descriptive statistics

Standard deviation6.653720329
Coefficient of variation (CV)0.2871558487
Kurtosis-0.8019090539
Mean23.17111199
Median Absolute Deviation (MAD)5.0939
Skewness-0.359188359
Sum98199.1726
Variance44.27199421
2020-08-15T21:27:51.832490image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
10.85051734.1%
 
18.11241413.3%
 
28.70411413.3%
 
27.02381393.3%
 
29.05881393.3%
 
26.84671393.3%
 
34.29961363.2%
 
11.12711363.2%
 
31.14711343.2%
 
15.31731343.2%
 
Other values (25)282666.7%
 
ValueCountFrequency (%) 
10.85051734.1%
 
11.12711363.2%
 
11.74011172.8%
 
11.94161252.9%
 
15.29931172.8%
 
ValueCountFrequency (%) 
34.29961363.2%
 
33.77821343.2%
 
31.14711343.2%
 
31.10481222.9%
 
30.73331242.9%
 

Longitude
Real number (ℝ≥0)

Distinct count31
Unique (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.40560110901369
Minimum71.1924
Maximum94.7278
Zeros0
Zeros (%)0.0%
Memory size33.1 KiB
2020-08-15T21:27:52.239432image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum71.1924
5-th percentile74.124
Q176.2711
median79.0193
Q385.3131
95-th percentile93.9063
Maximum94.7278
Range23.5354
Interquartile range (IQR)9.042

Descriptive statistics

Standard deviation6.821608566
Coefficient of variation (CV)0.08379777893
Kurtosis-0.8176304814
Mean81.40560111
Median Absolute Deviation (MAD)3.3054
Skewness0.7219177008
Sum344996.9375
Variance46.53434342
2020-08-15T21:27:52.632640image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
79.01932696.3%
 
75.71392686.3%
 
78.65692586.1%
 
92.93762295.4%
 
76.27111734.1%
 
77.10251413.3%
 
80.94621393.3%
 
76.08561393.3%
 
74.21791393.3%
 
78.29321363.2%
 
Other values (21)234755.4%
 
ValueCountFrequency (%) 
71.19241232.9%
 
73.0169761.8%
 
74.1241172.8%
 
74.21791393.3%
 
75.34121343.2%
 
ValueCountFrequency (%) 
94.72781092.6%
 
94.5624561.3%
 
93.90631192.8%
 
92.93762295.4%
 
92.65861172.8%
 

Total
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count2175
Unique (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7655.275837659273
Minimum1
Maximum357117
Zeros0
Zeros (%)0.0%
Memory size33.1 KiB
2020-08-15T21:27:53.030562image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q133
median413.5
Q33534
95-th percentile33963.8
Maximum357117
Range357116
Interquartile range (IQR)3501

Descriptive statistics

Standard deviation26238.18536
Coefficient of variation (CV)3.427464394
Kurtosis64.02940703
Mean7655.275838
Median Absolute Deviation (MAD)411.5
Skewness7.116421348
Sum32443059
Variance688442371
2020-08-15T21:27:53.538906image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
12746.5%
 
21072.5%
 
7892.1%
 
3882.1%
 
33651.5%
 
13421.0%
 
14370.9%
 
4330.8%
 
18300.7%
 
9250.6%
 
Other values (2165)344881.4%
 
ValueCountFrequency (%) 
12746.5%
 
21072.5%
 
3882.1%
 
4330.8%
 
5230.5%
 
ValueCountFrequency (%) 
3571171< 0.1%
 
3475021< 0.1%
 
3376071< 0.1%
 
3270311< 0.1%
 
3186951< 0.1%
 

Death
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count685
Unique (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.98961774421898
Minimum0
Maximum13132
Zeros1435
Zeros (%)33.9%
Memory size33.1 KiB
2020-08-15T21:27:54.166588image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q347.75
95-th percentile928.6
Maximum13132
Range13132
Interquartile range (IQR)47.75

Descriptive statistics

Standard deviation931.1634477
Coefficient of variation (CV)4.351442175
Kurtosis90.5452801
Mean213.9896177
Median Absolute Deviation (MAD)3
Skewness8.698983116
Sum906888
Variance867065.3664
2020-08-15T21:27:54.572470image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0143533.9%
 
14169.8%
 
31553.7%
 
21423.4%
 
41112.6%
 
5741.7%
 
11631.5%
 
7591.4%
 
6581.4%
 
8531.3%
 
Other values (675)167239.5%
 
ValueCountFrequency (%) 
0143533.9%
 
14169.8%
 
21423.4%
 
31553.7%
 
41112.6%
 
ValueCountFrequency (%) 
131321< 0.1%
 
128541< 0.1%
 
125561< 0.1%
 
122761< 0.1%
 
120301< 0.1%
 

Cured
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count1779
Unique (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4414.1998584237845
Minimum0
Maximum199967
Zeros611
Zeros (%)14.4%
Memory size33.1 KiB
2020-08-15T21:27:55.015069image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median117
Q31898
95-th percentile19912.3
Maximum199967
Range199967
Interquartile range (IQR)1893

Descriptive statistics

Standard deviation15590.68791
Coefficient of variation (CV)3.531939741
Kurtosis56.27588061
Mean4414.199858
Median Absolute Deviation (MAD)117
Skewness6.82804446
Sum18707379
Variance243069549.4
2020-08-15T21:27:55.467889image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
061114.4%
 
12115.0%
 
2942.2%
 
3781.8%
 
33531.3%
 
7481.1%
 
10390.9%
 
5370.9%
 
11360.8%
 
6330.8%
 
Other values (1769)299870.7%
 
ValueCountFrequency (%) 
061114.4%
 
12115.0%
 
2942.2%
 
3781.8%
 
4310.7%
 
ValueCountFrequency (%) 
1999671< 0.1%
 
1942531< 0.1%
 
1877691< 0.1%
 
1822171< 0.1%
 
1750291< 0.1%
 

New cases
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count871
Unique (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310.7720622935347
Minimum0
Maximum25297
Zeros1151
Zeros (%)27.2%
Memory size33.1 KiB
2020-08-15T21:27:55.937602image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17.5
Q3158
95-th percentile1550.6
Maximum25297
Range25297
Interquartile range (IQR)158

Descriptive statistics

Standard deviation1056.828981
Coefficient of variation (CV)3.400656332
Kurtosis115.8952751
Mean310.7720623
Median Absolute Deviation (MAD)17.5
Skewness8.476001987
Sum1317052
Variance1116887.496
2020-08-15T21:27:56.346414image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0115127.2%
 
11934.6%
 
21182.8%
 
3801.9%
 
4791.9%
 
5721.7%
 
6561.3%
 
7501.2%
 
8451.1%
 
10400.9%
 
Other values (861)235455.5%
 
ValueCountFrequency (%) 
0115127.2%
 
11934.6%
 
21182.8%
 
3801.9%
 
4791.9%
 
ValueCountFrequency (%) 
252971< 0.1%
 
138941< 0.1%
 
134381< 0.1%
 
123991< 0.1%
 
119231< 0.1%
 

New deaths
Real number (ℝ)

ZEROS

Distinct count144
Unique (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.328456819254365
Minimum-1
Maximum1409
Zeros2703
Zeros (%)63.8%
Memory size33.1 KiB
2020-08-15T21:27:56.804844image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q32
95-th percentile32
Maximum1409
Range1410
Interquartile range (IQR)2

Descriptive statistics

Standard deviation36.63183242
Coefficient of variation (CV)4.998573823
Kurtosis567.3802861
Mean7.328456819
Median Absolute Deviation (MAD)0
Skewness18.6144271
Sum31058
Variance1341.891147
2020-08-15T21:27:57.241332image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0270363.8%
 
13538.3%
 
21663.9%
 
31072.5%
 
4791.9%
 
6761.8%
 
5671.6%
 
9531.3%
 
8531.3%
 
7521.2%
 
Other values (134)52912.5%
 
ValueCountFrequency (%) 
-12< 0.1%
 
0270363.8%
 
13538.3%
 
21663.9%
 
31072.5%
 
ValueCountFrequency (%) 
14091< 0.1%
 
6681< 0.1%
 
5181< 0.1%
 
4931< 0.1%
 
4431< 0.1%
 

New recovered
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct count724
Unique (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.35889570552146
Minimum-1
Maximum13050
Zeros1735
Zeros (%)40.9%
Memory size33.1 KiB
2020-08-15T21:27:57.699140image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median4
Q382.75
95-th percentile933.75
Maximum13050
Range13051
Interquartile range (IQR)82.75

Descriptive statistics

Standard deviation712.3651874
Coefficient of variation (CV)3.60949115
Kurtosis77.19638963
Mean197.3588957
Median Absolute Deviation (MAD)4
Skewness7.530101714
Sum836407
Variance507464.1603
2020-08-15T21:27:58.125186image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0173540.9%
 
11573.7%
 
21022.4%
 
3832.0%
 
4771.8%
 
7541.3%
 
5511.2%
 
10400.9%
 
8390.9%
 
6330.8%
 
Other values (714)186744.1%
 
ValueCountFrequency (%) 
-130.1%
 
0173540.9%
 
11573.7%
 
21022.4%
 
3832.0%
 
ValueCountFrequency (%) 
130501< 0.1%
 
115461< 0.1%
 
83811< 0.1%
 
80181< 0.1%
 
77251< 0.1%
 

Interactions

2020-08-15T21:27:20.169412image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:20.637733image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:20.988635image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:21.357406image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:21.737066image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:22.227307image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:22.625993image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:23.053370image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:23.499209image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:23.850469image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:24.200083image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:24.615460image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:24.990966image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:25.373328image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:25.746525image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:26.254642image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:26.628795image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:26.992058image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:27.350553image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:27.907223image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:28.296789image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:28.874543image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:29.291130image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:29.750725image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:30.166917image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:30.535088image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:31.121977image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:31.610945image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:32.065184image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:32.449912image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:32.907688image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:33.391395image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:33.860307image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:34.324963image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:34.802335image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:35.297184image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:35.762287image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:36.214616image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:36.643795image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:37.113008image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:37.569985image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:37.953125image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:38.344471image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:38.790180image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:39.264116image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:39.745088image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:40.225780image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:40.615737image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:41.082161image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:41.520582image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:41.920741image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:42.328130image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:42.733856image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:43.144599image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:43.677143image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:44.065775image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:44.494557image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:44.897354image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:45.255239image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:45.629001image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:45.997989image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:46.468053image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:46.934250image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:47.439897image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-08-15T21:27:58.593927image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-15T21:27:59.145445image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-15T21:27:59.634576image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-15T21:28:00.210764image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-15T21:27:48.116004image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-15T21:27:48.739955image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

DateStateLatitudeLongitudeTotalDeathCuredNew casesNew deathsNew recovered
02020-01-30Kerala10.850576.2711100000
12020-01-31Kerala10.850576.2711100000
22020-02-01Kerala10.850576.2711200100
32020-02-02Kerala10.850576.2711300100
42020-02-03Kerala10.850576.2711300000
52020-02-04Kerala10.850576.2711300000
62020-02-05Kerala10.850576.2711300000
72020-02-06Kerala10.850576.2711300000
82020-02-07Kerala10.850576.2711300000
92020-02-08Kerala10.850576.2711300000

Last rows

DateStateLatitudeLongitudeTotalDeathCuredNew casesNew deathsNew recovered
42282020-07-25Odisha20.951785.0985226931201520115946808
42292020-07-25Puducherry11.941679.8083251535148395183
42302020-07-25Punjab31.147175.34121221628280964775355
42312020-07-25Rajasthan27.023874.217934178602245479588732
42322020-07-25Sikkim27.533088.5122477014217020
42332020-07-25Tamil Nadu11.127178.656919974933201432976785886504
42342020-07-25Telangana18.112479.01935246645540334164081007
42352020-07-25Tripura23.940891.98823759112131103159
42362020-07-25Uttar Pradesh26.846780.9462607711348377122667591909
42372020-07-25Uttarakhand30.066879.01935445603399000